MORTALITY AND INGROWTH PATTERN OF 
DIPTEROCARPS IN FOREST RECOVERY IN EAST 

KALIMANTAN
FARIDA H. SUSANTY, ENDANG SUHENDANG , I NENGAH SURATI JAYA2 2

and CECEP KUSMANA3

1Researcher on Dipterocarps Research Center, Samarinda 75119, Indonesia
2Department of  Forest Management, Institut Pertanian Bogor (IPB), Bogor 16680, Indonesia

3Department of  Silviculture, Institut Pertanian Bogor (IPB), Bogor 16680, Indonesia

Received /Accepted 3 May 2013 24 November 2014

ABSTRACT

 In primary and logged-over natural forest trees conditions tree structure, mortality and ingrowth rates , the such as 
will vary according to the species characteristic. Quantitative management variables become very important to 
support yield regulation tools for achieving sustainable forest management. The  objective was to determine study
mortality and ingrowth rates to formulate biometric characteristic variability Dipterocarps forest in logged-over of  
forests based on time series  The  was Labanan, East Kalimantan Province. Permanent data. study site located in 
measurement within -over were located to represent three , i.e  plots logged  forest  different logging techniques . a) 
reduced impact logging with diameter limit 50 cm (RIL 50); b RIL 60; c conventional logging; and d) primary forest )  ) 
as control  Total plot permanent area was  48 ha and was measured periodically every 2 years within 17 years after . about
logging. For data analysis purpose, trees were divided into Dipterocarps and non-Dipterocarps. two major groups, i.e. 
Range of  mortality rates for all species in logged-over forest were 2.5-29.3% per ha per 2 years which was close very 
to primary forest at year-5 after logging.  While range of  ingrowth rate for all species in logged-over forest were 
1.3-21.3% per ha per 2 years which were higher than those for the primary forest within 17 years. The mortality 
and ingrowth rates fluctuation of  Dipterocarps species group were different from those of  non-Dipterocarps. 

 : Keywords Dipterocarps, ingrowth, logged-over forest, mortality

INTRODUCTION

Lowland tropical rain forest is a natural forest 
with trees having typical characteristics, harboring 
the greatest species diversity in the world 
(Whitmore 1990; Richards 1996) and having 
numerous variations of  tree's dimensions (Prodan 
1968). Lowland tropical rain forests in Southeast 
Asia are dominated by Dipterocarpaceae (Ashton 
1982), therefore, it is often referred to as the 
Dipterocarp forests. Dipterocarp forest is a 
tropical rainforest inhabiting type A and type B 
climate  types are a,  cove ring Sumatera, 
Kalimantan, Sulawesi, North Maluku and Papua 
with the highest layer of  forest canopy filled with 
family Dipterocarpaceae, especially genus  Shorea,
Dipterocarpus, Dryobalanops Hopea and  (Ashton 
1982). Dipterocarp forest in West Malesia region 

is the most productive tropical forest types based 
on timber value (FAO 2001). In Indonesia, 
Dipterocarpaceae is the largest contributor (over 
25%) to commercial timber forests in decades, 
with volume of  50-100 m per ha, especially in 3 

Kalimantan (Sist . 2003)et al .
In primary and logged-over natural forest, the 

stand  stand conditions having differences in  
structure, species composition, tree density, 
canopy structure, mortality and ingrowth, will 
have varied  growth rates depending on tree the  
age after logging (Silva . 1995; Lewis . 2004; et al et al
Ishida . 2005). Recovery of  a logged-over et al  
forest happens in a long period after logging 
(Smith & Nichols 2005), which varies depending 
on deforestation rate and environmental carrying 
capacity (Muhdin . 2008). Natural production et al
forests in Indonesia have more than 50% of  
logged-over forests (Ditjen Planologi Kehutanan 
2011). Therefore, it is very important to know * Corresponding author  : fhsusanty@gmail.com

BIOTROPIA Vol. 22 No. 1, 2015:  11 - 23 DOI: 10.11598/btb.2015.22.1.297

11

mailto:fhsusanty@gmail.com


BIOTROPIA Vol. 22 No. 1, 2015

12

about the variation of  tree characteristics in a 
log ged-over forest. Biology and ecology 
information of  Dipterocarps is needed as 
scientific basis for developing effective forest 
management policies (Naito . 2008).et al

Forest biometric characteristics is among 
quantitative approaches to study the properties or 
characteristics of  forest trees in size (metric) for a 
specific biological dimension as the user 
identification by ratio and interval scale (Prodan 
1968). Input variables to determine quantitative 
tools are mostly provided by classical forest 
inventories on plots (Vanclay 2003; Gourlet-
Fleury . 2005). Most of  the early biometric et al
research are found in studies on plantations and 
temperate forests that do not have such 
complexity as tropical forests. The heterogeneity 
and complexity of  obstacles occur in the forms of  
diversity and variation of  conditions as well as 
limitations or lack of  long term data observation.  
Average rate of  mortality and its correlations to 
several reliable and measurable variables in size or 
site characteristics as input factors mostly 
determine the mortality model (Keister 1972; 
Hamilton & Edwards 1976; Monserud 1976; 
Hamilton 1994; Monserud & Sterba 1999; cited 
Flewelling & Monserud 2002). According to 
Chertov . (2005), a new paradigm in achieving et al
sustainable f orest management requires 
prediction of  effective growth forest tree 
dynamics involving aspects of  ecological 
characteristics.

To achieve sustainable forest management, 
preparation of  quantitative management tools 
such as yield regulation models becomes very 
important. Important variables needed to build 
the models are mortality and ingrowth rates of  
forest trees. This  aimed to determine study
mortality and ingrowth rates of  Dipterocarps and 
non-Dipterocarps spesies groups for 17 years 
after being logged, which rates will be used to 

formulat  biometric characteristics variability e of  
Dipterocarp forest in logged-over forests based 
on time series data.

MATERIALS AND METHODS

Study Site

 This study was carried out at Labanan research 
forest station (1˚49'-2˚10' N and 116˚7'-117˚27' 
E) located in Berau Region, East Kalimantan 
Province. According to Schmidt and Ferguson 
climate classification (1951), the study site was 
within type B climate (Q = 14.3–33.3%). Based 
on Koppen system classification the study site 
was within type AFA climate with many rainy days 
over in a year with mean annual precipitation 
about 1,800-3,000 mm/year. The highest 
monthly mean precipitation happened in January 
(242.5 mm) and the lowest is in August (90.9 mm).  
Maximum temperature rate happens in 
September and November (35 ºC) and the lowest 
was in February and August (21 ºC), with average 
temperature of  26 ºC.  The study site was located 
at 500 m above sea level (asl) and it was a relatively 
hilly forest. Soil type in Labanan research forest 
station consisted of  Ultisol (87.3%), Entisol 
(10.7%) and Incenptisol (2.0%).
 Labanan research forest station as a low land 
tropical forest is dominated by family 
Dipterocarpaceae, consisted of  7 genera i.e. 
Anisoptera, Cotylelobium, Dipterocarpus, Dryobalanops,  
Parashorea, Shorea Vatica and . Besides family 
Dipterocarpaceae, other dominant genera are 
also present in Labanan research forest station 
such as Sapotaceae, Meliaceae, Moraceae, Ebenaceae, 
Sapindaceae Leguminaceae and .  Among landscapes 
at the  Labanan research forest station was a 
swamp forest dominated by and Lophopetalum 
Shorea balangeran  (Saridan and Susanty 2005.

 

Figure 1. Study site-Labanan research forest station, Berau, East Kalimantan Province  



Mortality and ingrowth pattern of  dipterocarps in forest recovery in East Kalimantan Farida H. Susanty – et al. 

Data Collecting

 Permanent plots were set up in the logged-over 
forest as well as in the primary forest. The size of  
each plot was 200x200 m (4 ha) which was divided 
into 4 square subplots with size of  100x100 m 
(1 ha). The permanent plots were built in 4 
condition variations with total area of  48 ha.  
Measurements were carried out by census method 
for all species with limit diameter of  10 cm 
including number of  trees, tree species, stem 
diameter (diameter at breast height or 20 cm above 
buttresses) and number of  dead trees. Repeated 
measurements were performed every two years.
 Reduced impact logging can be defined as 
logging technique to minimize environment 
impact on forest trees and soils (Dykstra 2008).  
This technique is needed to preserve ecological 
aspect of  forest trees and to ensure sustainable 
yield of  production forest in the future.
 Data used in this study were data collected 
from 1990 to 2008.  Treatments applied on 
research plots were as follows:
a) RIL 50: reduced impact logging techniques 

with limit diameter of  50 cm, skid trail planning 
was based on contour maps and tree position 
as well as supervision of  tree felling and 
skidding (3 plots).

b) RIL 60: reduced impact logging techniques 
with limit diameter of  60 cm, skid trail planning 
was based on contour maps and tree position 
as well as supervision of  tree felling and 
skidding (3 plots).

c) CNV: conventional logging techniques with 
limit diameter of  60 cm, no skid trail planning, 
conducted without considering contour line 
maps or tree position, felling was done by 
loggers experiences (3 plots).

d) PF: primary forest as controls (3 plots).

Data Analysis

 Data organization was carried out using 
database software Microsoft Visual FoxPro 9.0, 
while data analysis was performed by using 
spreadsheet and SPSS 15.0.  Data were analyzed 
based on tree density and tree structure (stems per 
ha) as well as basal area (m per ha) with two major 2 

species groupings e.g. Dipterocarps and non-
Dipterocarps species. Calculations of  mortality 
and ingrowth  rates were performed every 2 years. 
Residual tree characteristics assessment was done 
by comparing variations in forest conditions by 

using different test mean values (t-test), analysis 
of  variance (ANOVA) and regression analysis. 
Regression equation tested was linear equations, 
polynomials, exponential and logarithmic. 
Criteria used for selecting the best equation were 
based on the regression coefficient (r), 
determination coefficient (R ) and the highest 2

value of  the smallest standard error (SE) (Steel & 
Torrie 1995) 

RESULTS AND DISCUSSION

Tree Density

 The dynamics of  logged-over forest within 
17 years were represented by number of  trees per 
hectare and basal area per hectare against tree 
fluctuations after-log ging using different 
logging techniques (Fig. 2). Mean values of  tree 
density at the initial conditions (pre-harvest) 
were compared using t-test and the results 
showed no significant differences in all study 
plots (t  <t ). Fluctuations of  tree density calc tab(0:05; 6)
and basal area of  logged-over forest would 
increase up to the initial conditions before logging 
(primary forest). Logged-over Dipterocarp forest 
recovery reached the conditions close to the 
primary forest at 9 years after-logging based on 
tree density and at 11 years after-logging based on 
basal area. According to Gourlet-Fleury . et al
(2005) and Kao and Iida (2006), tree density 
extremely increases at 5 to 7 years after-logging, 
which was explained by the increasing growth of  
open canopy in after-logging periods.
 Range of  tree density in the studied logged-
over forest was 461-647 stems per ha with average 
value of  531 stems per ha. Sianturi and Kanninen 
(2005) stated that at the period of  2 years after-
logging in Jambi forest, the tree density reached 
90% compared with tree density at the same 
forest before logging.
 Tree density in a primary forest in Sangai, 
Central Kalimantan (478-738 stems per ; ha
average of  583 stems per ) was similar to tree ha
density in Labanan forest (Susanty 2006). This 
study results were also similar to the condition in 
eastern Amazon forest showing average tree 
density of  480±96.6 stems per ha (Sist & Ferreira 
2007). While Gourlet-Fleury . (2005) et al
presented higher value of  tree density for French 
Guiana forest (625 stems per ha).

13



 Range of  basal area value in the studied 
logged-over forest was 19.35-31.84 m per ha.  As 2 

comparisons, basal area value in a several years 
after-logging forest in Central Kalimantan ranged 
from 16.4 to 26.7 m  per (Krisnawati 2001).  A 2

study conducted by Setiawan (2013) showed that 
basal area value in a logged-over forest in Muara 
Wahau, East Kalimantan ranged from 12.63 to 
32.57 m  per ha, while basal area value  in a 2

primary forest in Muara Wahau, East Kalimantan 
ranged from 27.80 to 32.57 m  per ha.  Basal area 2

value in Amazon forest had average of  28±4 m  2

per ha (Sist & Ferreira 2007).
 In  logged-over forests, tree density was about 
93-102% and basal area were about 81.0-88.8% 
compared with initial condition or before logging 
condition. Based on this result, the recovery 
pattern of  forest seemed to have positive trend 
with similar variations.  The  tree density after-
logging variable is important to know ecological 
sustainability (Sist . 2003; Smith & Nichols et al
2005; Muhdin 2012). This variable may indicate 
the level of  all logged-over forest which have well 
recovered, with assumption no interference or 
disturbance, thereafter. Different recovery 
patterns of  logged-over forest influenced 
ecological factor and forest tree condition. 
Growth monitoring on a 24 ha permanent sample 
plots in silviculture experiments in Amazônia 
(1980-1989) indicated that even in the same block 
of  concession area, forest recovery has different 
characteristics and patterns shown by fluctuations 
of  tree dynamic (Silva  1995).  Initial et al.
succession process in logged-over forests started 
from the end of  logging operational, followed by 
growing process in a time series function.

Mortality Rates

 The mortality level in different logging 
techniques is influenced by logging intensity 
based on number of  trees and volume per hectare 
which were felled by logging.  Logging intensity in 
RIL 50 had felled trees of  10.7 4.9 stems per ha +
with volume of  96.8 66.5 m  ha per;  RIL 60 had + 3

felled trees of  7.0 3.0 stems per ha  with volume +
of   56.5 23.3  m  per ha and CNV  had felled + 3

trees of   10.1 4.2 stems per ha with volume of   +
107.2 59.6 m  per ha (Sist & Bertault 1998).  + 3

These results indicated that logging intensity had 
a tendency to increase from RIL 60, RIL 50 and 
CNV logging techniques, respectively. In the 
logged-over forest, the mortality rates for all 
species ranged from 2.5 to 29.3% per ha per 2 
years (Table 1). The highest mortality rates 
occurred in year-1 and year-3 after logging then 
declined or was similar to mortality rates at the 
primary forest in year-5 after logging. The 
mortality rates in this study were higher than 
those in some studies; for example mortality rate 
in year-2 after logging in East Kalimantan forest 
was 2.5% per year (Primack . 1985; Nguyen-et al
The . 1998); mortality rate in a logged-over et al
forest in Papua New Guinea was 2.5% per ha per 
year (Mex 2005); mortality rate in a mixed 
Dipterocarp forests in Asia was 1.5% per year 
(Nguyen-The . 1998).  Compared to other et al
forest types, Dipterocarp forest has lower 
mortality rate than that of  peat swamp forest 
which was 6.13% per year and of  heath forests 
which was 4.26% per year (Nishimua . 2006).et al
 Mortality rates in forest with different logging 
techniques indicated higher logging intensity for 
both Dipterocarps and non-Dipterocarps (Fig. 3).  

BIOTROPIA Vol. 22 No. 1, 2015

14

Figure  . Forest trees for all species with different logging techniques based on (a) average tree density (stems per ha) and (b) 2
average basal area (m per ha)2 



Non-Dipterocarps species have a tendency to 
have higher mortality rate than Dipterocarps.  
Mortality rate for both Dipterocarps and non-
Dipterocarps in primary forest ranged from 2.0 to 
6.0% per ha per  2 years with average of  3.29% per 
ha per 2 years.  The increasing logging intensity 
was negatively correlated with stem densities, 
species abundance and biodiversity, where prior 
changes in the logged-over forest for the first 
5–10 years after logging were gradually occurred 
in successive samples (Kariuki . 2006). The et al
mortality rates of  trees in a forest is relatively high 
within 7 years, indicating that damaging effects of  
logging would permanently influence trees, either 
directly or indirectly. In other words, within 7 years 
after logging, the remaining trees will begin to 
recover.
 Fluctuations of  mortality rate within 17 years 
after logging indicated that the mortality rates 
decreased over years for both Dipterocarps and 

non-Dipterocarps. A theoretical frame for 
ingrowth limitations, climatic variation or many 
factors became the constraints to predict tree-
level change (Harcombe . 2002). Several et al
variables causing differences of  mortality rates 
were logging intensity, initial structure and 
dominant species composition. The mortality 
degree is different for every tree's stage in the 
forests.

Ingrowth Rates
 Ingrowth rates in logged-over forest have the 
same tendency with mortality rates that correlate 
with level of  logging intensity (Table 2). In this 
case, conventional logging technique can still 
control the total felled trees to only cut down trees 
having limit diameter of  60 cm. The ingrowth 
rates in logged-over forest (1.3-21.3% per ha per 2 
years) were higher than those in primary forest 
(0.6-4.7% per ha per 2 years). The highest 

15

Table .  Tree mortality rates (% per ha per 2 years) for all species1  

Forest  
Condition  

PAL 1  PAL 3  PAL 5  PAL 7  PAL 9  PAL 11  PAL 13  PAL 15  PAL 17
(% per  ha  per  2  years)  

RIL50 Mean  23.7  10.9  4.3  5.8  3.0  2.9  2.7  3.4  3.3
 SD  10.6  6.2  1.4  1.2  0.7  1.1  1.1  1.7  1.5
RIL60 Mean  22.3  8.3  4.4  5.5  3.3  2.5  2.5  2.5  3.5
 SD  8.1  3.5  1.1  1.3  0.9  0.8  1.1  1.3  2.0
CNV Mean  29.3  12.8  2.8  6.8  3.3  3.6  2.8  2.9  4.3
 SD  9.2  8.0  1.3  2.3  0.9  2.1  1.0  1.9  0.4

  P1  P3 P5 P7 P9 P11 P13  P15  P17  
PF Mean  2.9  3.2  4.7  6.0  3.2  3.4  2.7  2.0  3.2
 SD  1.0  1.3  5.1  1.8  1.2  1.5  1.5  1.4  2.0
Notes : PAL = period after logging (years) 
            SD = Standard deviation
            P = monitoring period (years)

Figure  . Mortality rates (stems per ha per 2 years) of  trees with different logging techniques for species group (a) 3
Dipterocarps and (b) non-Dipterocarps

Mortality and ingrowth pattern of  dipterocarps in forest recovery in East Kalimantan Farida H. Susanty – et al. 



ingrowth rate for all species occurred at year-5 to 
year-7 (Table 2). After logging, the ingrowth rates 
increased substantially during the first 5 years. 
During the next five years, there was a sharp 
decrease.  Hardiansyah . (2005) stated that at et al
year-1 after logging in Jambi, ingrowth rate was 
0.19-2.89% per year with average 2.1% per year.  
Meanwhile, in Papua New Guinea Mex (2005) 
stated that at year-13 after logging the trees had 
smaller ingrowth rate with mean of  41 stems per 
ha. In the primary forest, ingrowth rate 
fluctuation were relatively low i.e. 2.0-4.7% per 
ha per 2 years with average of  2.5% per ha per 2 
years. Fluctuations of  ingrowth rates for 
Dipterocarps and non-Dipterocarps species in 
logged-over forest with different logging 
techniques had different patterns (Fig. 4). In this 
study 68 tree families had been identified 
(Appendix 1), in which there were 8 genera of  
Dipterocarps (Appendix 2) namely Anisoptera, 
Cotylelobium, Dipterocarpus, Dryobalanops, Hopea, 
Parashorea, Shorea,  Vatica and  consisted of  92 

species. Non-Dipterocarpaceae trees  dominating 
the logged-over forest were especially pioneer 
species such as  sp.  This result was Macaranga
similar to the research results in Brazilian Amazon 
forest which showed that the ingrowth rate 
increased in the first 8 years after logging 
provided there were more light in the under- 
storey which stimulated ingrowth (Silva . et al
1995). Ingrowth can soar through the canopy 
opening after logging provided there were more 
growing space and ingrowth would decrease 
in line with the competition among trees 
(Gourlet-Fleury . 2005).  Logged-over forests et al
would have good recovery with high growth rate 
at  year-3 after logging (Kao & Iida 2006).  The 
first highest ingrowth rate occurred in year-7 
indicating high growth regeneration which in line 
with the beginning of  mortality rate decline in 
year-7. The second highest ingrowth rate 
occurred in year-15 which might be caused by 
recovery process of  forest trees after being 
logged-over.

BIOTROPIA Vol. 22 No. 1, 2015

16

Table . Tree ingrowth rates (% per ha per 2 years) for all species2

Forest  
Condition  

PAL 1  PAL 3  PAL5  PAL 7  PAL 9  PAL11  PAL 13  PAL 15  PAL17
(% per ha  per  2  years1)  

RIL50  Mean  1.8  9.0  21.3  14.5  8.8  2.8  3.4  6.5  0.6
 SD  0.6  4.9  25.3  8.9  4.4  2.4  1.7  2.7  0.2
RIL60  Mean  2.1  6.3  9.0  10.5  7.9  1.6  3.5  9.8  0.7

 SD  1.0  2.3  2.3  3.3  3.8  1.0  1.4  5.7  0.1
CNV  Mean  1.3  12.7  19.1  19.6  7.8  3.5  5.2  5.4  0.7

 SD  0.6  8.5  13.4  8.2  4.8  1.2  2.6  1.9  0.2

  P1 P3 P5 P7 P9 P11 P13  P15  P17  
PF  Mean  2.1  3.8  3.7  4.7  2.0  1.1  1.6  4.5  0.7

 SD  1.0  1.9  1.9  2.2  1.1  1.1  0.9  2.7  0.6

Figure . Ingrowth rates (stems per ha per 2 years) of  trees with different logging techniques for species group (a) 4
Dipterocarps and (b) non-Dipterocarps

Notes : PAL = period after logging (years) 
            SD = Standard deviation
            P = monitoring period (years)



Species  
grouping   

Regression equation  r  R2  SE  

D LOF  y(M)  = -0.039x3  + 1.295x2  -  13.177x + 43.501  0.8955  0.8020  4.855

  y(I)  = 0.011x3  -  0.3676x2  + 3.2295x + 0.2946  0.5326  0.2837  3.552

 PF   y(M)  = 0.0073x3  -  0.2272x2  + 1.8366x + 0.8951  0.6448  0.4158  1.577

  y(I)  = 5.107e
-0.133x

 0.4106  0.1686  0.371

ND LOF  y(M)  = -0.0779x3  + 2.6842x2  -  28.4x + 101.3  0.9071  0.8229  9.985

  y(I)  = 0.0667x
3

 -  2.1667x
2

 + 18.792x -  11.094  0.6317  0.3990  14.765

 PF   y(M)  = 0.0196x
3

 -  0.5982x
2

 + 4.8012x + 4.3734  0.6118  0.3743  4.142

  
y(I)

 
= 13.873e-0.082x

 
0.4900

 
0.2401

 
0.339

All species  LOF  y(M)  = -0.1174x3  + 3.9793x2  -  41.577x + 144.81  0.9160  0.8391  13.686

  y(I)  = 0.0778x3  -  2.5343x2  + 22.022x -  10.799  0.6363  0.4049  17.288

 PF   y(M)  = 0.0269x
3

 -  0.8254x
2

 + 6.6378x + 5.2685  0.6375  0.4064  5.474
y(I) = 17.636e-0.078x 0.4775 0.2280 0.3359

 Canopy gap occurred in a logged-over forest 
can function as a catalyst for tree ingrowth until 
certain condition. This phenomenon would then 
decrease due to competition in population 
(Gourlet-Fleury . 2005; Kao & Iida 2006), et al
which would explain why natural ingrowth 
formed in the period of  11-17 years in the forest 
recovery. Different species group will be affected 
variably to competition for light based on 
respective tolerable range (Harcombe . 2002).  et al
Research on forest tree in Peninsular Malaysia 
showed variations in regenerated tree by class 
diameter of  trees in the logged-over forest area 
(Seng . 2004).et al

Period after Logging to Mortality and 
Ingrowth Pattern

 Results of  analysis of  variance (ANOVA) 
showed that different harvesting techniques had 
no significant effect on mortality rate and 
ingrowth rate for all species.  However, 
descriptively, the mortality and ingrowth rates 
were higher at year-1 and year-3 after logging 
(Tables 1 and 2).  From this study it was indicated 
that RIL 50 seemed to be the best technique to 
increase ingrowth rate, but RIL 60 was the 
selected technique to decrease the mortality rate 
after logging.
 Regression equations were developed to show 
relationship between after-logging period to 

mortality and ingrowth rates for each species 
group (Table 3).  These equations showed that 
after-logging period correlated with mortality rate 
in logged-over forest for both Dipterocarps and 
non-Dipterocarps. Recovery period after logging 
will affect the level of  mortality and ingrowth 
rates for Dipterocarps, non-Dipterocarps and all 
species groups (Fig. 4). All species group in 
logged-over forest had polynomial regression 
pattern for mortality and ingrowth rates, but 
ingrowth rates on primary forest had negative 
exponential pattern.
 High mortality rate was shown to happen in 
Dipterocarps in early years of  logging and 
seemed to be caused by Dipterocarps being a 
major commercial species. Meanwhile, high 
mortality rate for non-Dipterocarps seemed 
to be due to forest area clearing related to skidding 
trails and logging yard as well as to logging 
operation.
 Mortality rates for Dipterocarps and non-
Dipterocarps as either direct or indirect effect of  
logging was continued until the third year.  
Ingrowth rates of  non-Dipterocarps species 
group was higher than that of  Dipterocarps. 
This was correlated with general growth or 
increment characteristics of  total tree basal 
area increment of  non-Dipterocarps which was 
higher than that of  Dipterocarps species 
groups (Silva . 2002; Susanty & Suhendang et al
2013).

17

Table . Regression equations between after-logging period (x) to mortality rates (M) and ingrowth rates (I) for Dipterocarps 3
(D), non-Dipterocarps (ND) and all species

Notes: LOF : Logged-over forest; PF : Primary Forest; x : after-logging period (years); y(M) : Mortality rates (stems per ha per 2 years); y 
 (I) : Ingrowth rates (stems per ha per 2 years) 

Mortality and ingrowth pattern of  dipterocarps in forest recovery in East Kalimantan Farida H. Susanty – et al. 



 Results of  regression analysis (Table 3; Fig. 5) 
showed that after-logging period was significantly 
cor r elated  to mor tality rate s for both 
Dipterocarps and non-Dipterocarps. The 
function of  recovery time after logging would 
reduce mortality rates from logging effect into 
natural mortality rates. Ingrowth rates after 
logging for both Dipterocarps and non-
Dipterocarps could not be explained only from 
recovery time after logging. Opportunity for 
seedlings and saplings to grow is influenced by 
biocharacteristics of  each species and by 
distribution pattern (Husch  2003; Adam & et al.
Kolbs 2005; Gersonde & O'Hara 2005).  Natural 
characteristics of  mortality and ingrowth rates 
shown by primary forest conditions indicated that 
mortality rate tended to be higher than ingrowth 

rate within the 17 years of  measurements for both 
Dipterocarps and non-Dipterocarps.  In natural 
forest, growth will increase the dimension of  
individual trees while reducing the number of  
trees due to competition of  growing space.
 Results from this research provided the first 
quantitative information for management 
planning in Labanan forest. To achieve higher 
ingrowth rate and minimize mortality rate until 
year-3 after-logging, the Reduced Impact 
Logging technique (RIL 50 and RIL 60) was 
better than conventional logging technique. 
These data serve as basic information needed to 
start forest management planning and provide 
useful guide for choosing suitable planning 
system. This study highlighted the need to 
consider silviculture treatment to stimulate 

BIOTROPIA Vol. 22 No. 1, 2015

18

Figure .  Patterns of  mortality and ingrowth rates (stems per ha per 2 years) on logged-over forest  (LOF) and primary forest 5
(PF) for species groups (a) Dipterocarps; (b) non-Dipterocarps; (c) all species 



growth rates in forest areas, especially to increase 
wood production.  The methods described in this 
study can be enhanced to involve a more detail 
species grouping for increasing model accuracy 
and to accommodate high species diversity 
(Phillips . 2002; Valle . 2006). In et al et al
Dipterocarp forest, valuation of  tree recovery 
could be assessed by tree structure model, 
mortality rate and ingrowth rate, involving species 
characteristic by species grouping.  

 
CONCLUSIONS

  Residual trees in Dipterocarp forest in year-17 
after-logging  based on had been well recovered
density and basal area value that reached more 
than 85% close to the initial condition.  Mortality 
rates in logged-over forest would be similar to that 
in the primary forest at year-7 after-logging, while 
ingrowth rate in logged-over forest would be 
similar to that in the primary forest at year-17 
after- logging.  Fluctuations in mortality and 
ingrowth rates in logged-over forest reached 
equilibrium conditions at year-7 after-logging. In 
the subsequent years,  ingrowth rate was higher 
than mortality rate. Fluctuations of  mortality and 
ingrowth rates in the primary forests were 
relatively low.  Recovery period for trees in 
logged-over forest had an important effect to 
decrease mortality rates for both Dipterocarps 
and non-Dipterocarps.  Different types of  
logging techniques (RIL 50, RIL 60 and 
conventional logging) with variations in logging 
intensity 7-14 stems per hectare did not have 
significant influence on recovery of  density and 
basal area value, as well as on mortality and 
ingrowth rates. The Reduced Impact Logging 
technique was better than conventional logging 
technique to achieve higher ingrowth rate and 
minimize mortality rate until year-3 after-logging. 
Quantitative dimensions of  forest trees at 17 years 
after logging will be close with the primary forests, 
but still dominated by the non-Dipterocarps 
species.

ACKNOWLEDGEMENTS

 The authors thanked the Forest Research and 
D e v e l o p m e n t  A g e n c y  ( F O R DA )  a n d  
Dipterocarps Research Center (DIREC) for 

permitting the use of  data measurement of  
permanent sample plots in the Labanan forest 
station.

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Mortality and ingrowth pattern of  dipterocarps in forest recovery in East Kalimantan Farida H. Susanty – et al. 



Appendix 1. List of  species by family in Labanan Forest  

Family  Famil y  Famil y  Famil y  
Actinidiaceae  Dilleniaceae  Moraceae  Saxifragaceae  
Alangiaceae  Dipterocarpaceae  Myristicaceae  Simaroubaceae  
Amonaceae  Ebenaceae  Myrsinaceae  Sonneratiaceae  
Anacardiaceae  Elaeocarpaceae  Myrtaceae  Sterculiaceae  
Annonaceae  Euphorbiaceae  Ochnaceae  Symplocaceae  
Apocynaceae  Fagaceae  Olacaceae  Theaceae  
Aquifoliaceae  Flacourtiaceae  Oleaceae  Thymelaeaceae  
Araucariaceae  Clusiaceae  Oxalidaceae  Tiliaceae  
Bignoniaceae  Hypericaceae  Polygalaceae  Ulmaceae  
Bombacaceae  Icacinaceae  Proteaceae  Urticaceae  
Burseraceae  Juglandaceae  Rhamnaceae  Verbenaceae  
Caesalpiniaceae  Lauraceae  Rhizophoraceae
Celastraceae  Lecythidaceae  Rosaceae  
Chrysobalanaceae  Fabaceae  Rubiaceae  
Combretaceae

 
Loganiaceae

 
Rutaceae

 
Connaraceae

 
Lythraceae

 
Sapindaceae

 
Convolvulaceae

 
Magnoliaceae

 
Sapotaceae

 Crypteroniceae
 

Melastomataceae
 

Sapuidaceae
 Datiscaceae Meliaceae Sarcotheca

BIOTROPIA Vol. 22 No. 1, 2015

22



47  Shorea bentongenensis  
48  Shorea confusa  
49  Shorea exelliptica
50

 
Shorea faguetiana

51
 
Shorea falciferoides

52
 
Shorea fallax

 
53

 
Shorea guiso

 54
 
Shorea hopeifolia

 55
 
Shorea inappendiculata

56
 
Shorea johorensis

57
 
Shorea laevis

 58
 
Shorea lamellata

59
 
Shorea leprosula

 60
 
Shorea leptoderma

61
 
Shorea longisperma

62
 
Shorea macrophylla

63
 
Shorea macroptera

64
 
Shorea maxwelliana

65
 
Shorea mecistopteryx

66
 
Shorea multiflora

67
 
Shorea ochracea

 68
 
Shorea ovalis ssp ovalis

69
 
Shorea parvifolia

 70 Shorea parvistipulata

Appendix 2.  List of  Dipterocarps species

No Species  No  Species  
1  Anisoptera costata   
2  Anisoptera laevis
3  Anisoptera sp  
4  Cotylelobium melanoxylon
5  Cotylelobium sp  
6  Dipterocarpus acutangulus
7  Dipterocarpus caudiferus
8  Dipterocarpus confertus
9  Dipterocarpus conformis

10  Dipterocarpus costulatus
11  Dipterocarpus elongatus
12  Dipterocarpus fusiformis
13  Dipterocarpus glabrigemmatus
14  Dipterocarpus gracilis
15  Dipterocarpus grandiflorus
16

 
Dipterocarpus hasseltii

17
 

Dipterocarpus humeratus
18

 
Dipterocarpus mundus

19
 

Dipterocarpus pachyphyllus
20

 
Dipterocarpus palembanica

21
 

Dipterocarpus stellatus 
22

 
Dipterocarpus tempehes

23
 

Dipterocarpus verrucosus
24

 
Dipterocarpus sp

 25
 

Dryobalanops beccarii
26

 
Dryobalanops lanceolata

27
 

Dryobalanops sp
 28

 
Hopea bracteata

 29
 

Hopea cernua
 30

 
Hopea dryobalanoides

31
 

Hopea ferruginea
32

 
Hopea mengarawan

33
 

Hopea nervosa
34

 
Hopea pachycarpa

35 Hopea rudiformis
 82  Shorea sp  

83  Vatica albiramis  
84  Vatica micrantha  
85  Vatica nitens  
86  Vatica oblongifolia
87  Vatica odorata
88  Vatic a rassak
89  Vatica sarawakensis
90  Vatica umbonata
91  Vatica vinosa
92 Vatica sp

36  Hopea sangal
37  Hopea semicuneata
38  Hopea sp  
39  Parashorea malaanonan
40  Parashorea smythiesii
41  Parashorea sp  
42  Shorea agamii ssp agamii
43  Shorea almon
44  Shorea angustifolia
45  Shorea atrinervosa
46  Shorea beccariana  

71  Shorea patoiensis  
72  Shorea pauciflora  
73  Shorea pinanga  
74  Shorea scrobiculata  
75  Shorea semicuneata  
76  Shorea seminis  
77  Shorea smithiana  
78  Shorea superba  
79  Shorea symingtonii  
80  Shorea virescens  
81 Shorea xanthophylla

23

Mortality and ingrowth pattern of  dipterocarps in forest recovery in East Kalimantan Farida H. Susanty – et al.